SSFD+: A robust two-stage face detector

Lei Shi, Xiang Xu, Ioannis A. Kakadiaris

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Face detectors based on deep learning have demonstrated great progress in detecting multi-scale faces by using multi-scale feature maps and input pyramids. However, using input pyramids and multi-scale feature maps increases the training difficulty and complexity of the network. In this paper, we focus on achieving comparable performance and simplifying the network architecture for detecting multi-scale faces. To enable network learning of multi-scale facial features from a single-scale feature map and a single-scale input image: 1) we conducted a comparative study to investigate which layer contributes more to detecting multi-scale faces and 2) we designed and implemented a simple network structure to improve the performance of detecting multi-scale faces by incorporating additional contextual information. SSFD+ achieves mAPs of (91.3%, 90.3%, 83.1%) and (92.4%, 90.9%, 83.7%) on the (easy, medium, and hard) subsets of the WIDER FACE validation and testing datasets, respectively, and promising results on the FDDB, PASCAL Faces, and AFW datasets.

Original languageEnglish (US)
Article number8762151
Pages (from-to)181-191
Number of pages11
JournalIEEE Transactions on Biometrics, Behavior, and Identity Science
Issue number3
StatePublished - Jul 2019


  • Face detector
  • multi-scale

ASJC Scopus subject areas

  • Instrumentation
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Artificial Intelligence


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